OpenRaiser/Intern-Atlas huggingface dataset:
Teaching computer code at school
Venue: arXiv cs.CY 2017
Year: 2017

In today's education systems, there is a deep concern about the importance of teaching code and computer programming in schools. Moving digital learning from a simple use of tools to understanding the processes of the internal functioning of these tools is an old / new debate originated with the digital laboratories of the 1960. Today, it is emerging again under impulse of the large - scale public sphere digitalization and the new constructivist education theories. Teachers and educators discuss not only the viability of code teaching in the classroom, but also the intellectual and cognitive advantages for students. The debate thus takes several orientations and is resourced in the entanglement of arguments and interpretations of any order, technical, educational, cultural, cognitive and psychological. However, that phenomenon which undoubtedly augurs for a profound transformation in the future models of learning and teaching , is predicting a new and almost congenital digital humanism
Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?
Venue: arXiv cs.CR 2017
Year: 2017

The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into this new computing paradigm is a major technical challenge today. However, what are the security problems in IoT that we can solve using existing security principles? And, what are the new problems and challenges in this space that require new security mechanisms? This article summarizes the intellectual similarities and differences between classic information technology security research and IoT security research.
Joint Rate Control and Power Allocation for Non-Orthogonal Multiple Access Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently-developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access (OMA) schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.
Fully reliable error control for evolutionary problems
Venue: arXiv cs.NA 2017
Year: 2017

This work is focused on the application of functional-type a posteriori error estimates and corresponding indicators to a class of time-dependent problems. We consider the algorithmic part of their derivation and implementation and also discuss the numerical properties of these bounds that comply with obtained numerical results. This paper examines two different methods of approximate solution reconstruction for evolutionary models, i.e., a time-marching technique and a space-time approach. The first part of the study presents an algorithm for global minimization of the majorant on each of discretization time-cylinders (time-slabs), the effectiveness of this algorithm is confirmed by extensive numerical tests. In the second part of the publication, the application of functional error estimates is discussed with respect to a space-time approach. It is followed by a set of extensive numerical tests that demonstrate the efficiency of proposed error control method.
Dictionary-based Monitoring of Premature Ventricular Contractions: An Ultra-Low-Cost Point-of-Care Service
Venue: arXiv cs.LG 2017
Year: 2017

While cardiovascular diseases (CVDs) are prevalent across economic strata, the economically disadvantaged population is disproportionately affected due to the high cost of traditional CVD management. Accordingly, developing an ultra-low-cost alternative, affordable even to groups at the bottom of the economic pyramid, has emerged as a societal imperative. Against this backdrop, we propose an inexpensive yet accurate home-based electrocardiogram(ECG) monitoring service. Specifically, we seek to provide point-of-care monitoring of premature ventricular contractions (PVCs), high frequency of which could indicate the onset of potentially fatal arrhythmia. Note that a traditional telecardiology system acquires the ECG, transmits it to a professional diagnostic centre without processing, and nearly achieves the diagnostic accuracy of a bedside setup, albeit at high bandwidth cost. In this context, we aim at reducing cost without significantly sacrificing reliability. To this end, we develop a dictionary-based algorithm that detects with high sensitivity the anomalous beats only which are then transmitted. We further compress those transmitted beats using class-specific dictionaries subject to suitable reconstruction/diagnostic fidelity. Such a scheme would not only reduce the overall bandwidth requirement, but also localising anomalous beats, thereby reducing physicians' burden. Finally, using Monte Carlo cross validation on MIT/BIH arrhythmia database, we evaluate the performance of the proposed system. In particular, with a sensitivity target of at most one undetected PVC in one hundred beats, and a percentage root mean squared difference less than 9% (a clinically acceptable level of fidelity), we achieved about 99.15% reduction in bandwidth cost, equivalent to 118-fold savings over traditional telecardiology.
SNMP for Common Lisp
Venue: arXiv cs.NI 2017
Year: 2017

Simple Network Management Protocol (SNMP) is widely used for management of Internet-based network today. In Lisp community, there're large Lisp-based applications which may need be monitored, and there're Lispers who may need to monitor other remote systems which are either Lisp-based or not. However, the relationship between Lisp and SNMP haven't been studied enough during past 20 years. The cl-net-snmp project has developed a new Common Lisp package which implemented the SNMP protocol. On client side, it can be used to query remote SNMP peers, and on server side, it brings SNMP capability into Common Lisp based applications, which could be monitored from remote through any SNMP-based management system. It's also a flexible platform for researches on network management and SNMP itself. But the most important, this project tries to prove: Common Lisp is the most fit language to implement SNMP. Different from other exist SNMP projects on Common Lisp, cl-net-snmp is clearly targeted on full SNMP protocol support include SNMPv3 and server-side work (agent). During the development, an general ASN.1 compiler and runtime package and an portable UDP networking package are also implemented, which would be useful for other related projects. In this paper, the author first introduces the SNMP protocol and a quick tutorial of cl-net-snmp on both client and server sides, and then the Lisp native design and the implementation details of the ASN.1 and SNMP package, especially the "code generation"' approach on compiling SNMP MIB definitions from ASN.1 into Common Lisp.
Fast algorithms for anti-distance matrices as a generalization of Boolean matrices
Venue: arXiv cs.DM 2017
Year: 2017

We show that Boolean matrix multiplication, computed as a sum of products of column vectors with row vectors, is essentially the same as Warshall's algorithm for computing the transitive closure matrix of a graph from its adjacency matrix. Warshall's algorithm can be generalized to Floyd's algorithm for computing the distance matrix of a graph with weighted edges. We will generalize Boolean matrices in the same way, keeping matrix multiplication essentially equivalent to the Floyd-Warshall algorithm. This way, we get matrices over a semiring, which are similar to the so-called "funny matrices". We discuss our implementation of operations on Boolean matrices and on their generalization, which make use of vector instructions.
Matrix-product structure of repeated-root constacyclic codes over finite fields
Venue: arXiv cs.IT 2017
Year: 2017

For any prime number $p$, positive integers $m, k, n$ satisfying ${\rm gcd}(p,n)=1$ and $λ_0\in \mathbb{F}_{p^m}^\times$, we prove that any $λ_0^{p^k}$-constacyclic code of length $p^kn$ over the finite field $\mathbb{F}_{p^m}$ is monomially equivalent to a matrix-product code of a nested sequence of $p^k$ $λ_0$-constacyclic codes with length $n$ over $\mathbb{F}_{p^m}$.
Deep Investigation of Cross-Language Plagiarism Detection Methods
Venue: arXiv cs.CL 2017
Year: 2017

This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.
A Geometric Analysis Method for Evaluation of Coexistence between DSRC and Wi-Fi at 5.9 GHz
Venue: arXiv cs.NI 2017
Year: 2017

Co-channel coexistence between Dedicated Short-Range Communications (DSRC) and Wi-Fi needs thorough study. The reason is that although the 5.850-5.925 GHz (5.9 GHz) band has been reserved for DSRC so far, the U.S. government is moving swiftly on opening the band to be shared with Wi-Fi. However, most prior work lacks sufficient scientific rigor by relying on performance metrics such as packet delivery rate (PDR) and packet delay that cannot accurately measure performance of a vehicular network that primarily uses broadcast in dissemination of packets. Precise analysis of such broadcast operation is essential for rigorous investigation of DSRC-Wi-Fi coexistence because most safety-critical applications of DSRC operate based on broadcast. This paper proposes a new metric that can more accurately characterize the performance of a broadcast-based DSRC network. The new metric is used to (i) characterize coexistence of DSRC with IEEE 802.11ac-based Wi-Fi and (ii) suggest selection of key medium access control (MAC) parameters for DSRC: inter-broadcast interval (IBI) and contention window (CW).
Characterizing Fan Behavior to Study Para Social Breakups
Venue: arXiv cs.SI 2017
Year: 2017

Celebrity and fandom have been studied extensively in real life. However, with more and more celebrities using social media, the dynamics of interaction between celebrities and fans has changed. Using data from a set of 57,000 fans for the top followed celebrities on Twitter, we define a wide range of features based on their Twitter activity. Using factor analysis we find the most important factors that underlie fan behavior. Using these factors, we conduct analysis on (i) understanding fan behavior by gender \& age, and (ii) para-social breakup behavior. We find that (i) fandom is a social phenomenon, (ii) female fans are often more devoted and younger fans are more active & social, and (iii) the most devoted fans are more likely to be involved in a para-social breakup. Our findings confirm existing research on para-social interactions. Given the scale of our study and dependence on non-reactive data, our paper opens new avenues for research in para-social interactions.
Modeling and Simulation of the Dynamics of the Quick Return Mechanism: A Bond Graph Approach
Venue: arXiv cs.RO 2017
Year: 2017

This paper applies the multibond graph approach for rigid multibody systems to model the dynamics of general spatial mechanisms. The commonly used quick return mechanism which comprises of revolute as well as prismatic joints has been chosen as a representative example to demonstrate the application of this technique and its resulting advantages. In this work, the links of the quick return mechanism are modeled as rigid bodies. The rigid links are then coupled at the joints based on the nature of constraint. This alternative method of formulation of system dynamics, using Bond Graphs, offers a rich set of features that include pictorial representation of the dynamics of translation and rotation for each link of the mechanism in the inertial frame, representation and handling of constraints at the joints, depiction of causality, obtaining dynamic reaction forces and moments at various locations in the mechanism and so on. Yet another advantage of this approach is that the coding for simulation can be carried out directly from the Bond Graph in an algorithmic manner, without deriving system equations. In this work, the program code for simulation is written in MATLAB. The vector and tensor operations are conveniently represented in MATLAB, resulting in a compact and optimized code. The simulation results are plotted and discussed in detail.
Towards a Knowledge Graph based Speech Interface
Venue: arXiv cs.HC 2017
Year: 2017

Applications which use human speech as an input require a speech interface with high recognition accuracy. The words or phrases in the recognised text are annotated with a machine-understandable meaning and linked to knowledge graphs for further processing by the target application. These semantic annotations of recognised words can be represented as a subject-predicate-object triples which collectively form a graph often referred to as a knowledge graph. This type of knowledge representation facilitates to use speech interfaces with any spoken input application, since the information is represented in logical, semantic form, retrieving and storing can be followed using any web standard query languages. In this work, we develop a methodology for linking speech input to knowledge graphs and study the impact of recognition errors in the overall process. We show that for a corpus with lower WER, the annotation and linking of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight, a tool to interlink text documents with the linked open data is used to link the speech recognition output to the DBpedia knowledge graph. Such a knowledge-based speech recognition interface is useful for applications such as question answering or spoken dialog systems.
Filtering Variational Objectives
Venue: arXiv cs.LG 2017
Year: 2017

When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as the ELBO, but can exploit a model's sequential structure to form tighter bounds. We present results that relate the tightness of FIVO's bound to the variance of the particle filter's estimator by considering the generic case of bounds defined as log-transformed likelihood estimators. Experimentally, we show that training with FIVO results in substantial improvements over training the same model architecture with the ELBO on sequential data.
Stochastic Assume-Guarantee Contracts for Cyber-Physical System Design Under Probabilistic Requirements
Venue: arXiv cs.SY 2017
Year: 2017

We develop an assume-guarantee contract framework for the design of cyber-physical systems, modeled as closed-loop control systems, under probabilistic requirements. We use a variant of signal temporal logic, namely, Stochastic Signal Temporal Logic (StSTL) to specify system behaviors as well as contract assumptions and guarantees, thus enabling automatic reasoning about requirements of stochastic systems. Given a stochastic linear system representation and a set of requirements captured by bounded StSTL contracts, we propose algorithms that can check contract compatibility, consistency, and refinement, and generate a controller to guarantee that a contract is satisfied, following a stochastic model predictive control approach. Our algorithms leverage encodings of the verification and control synthesis tasks into mixed integer optimization problems, and conservative approximations of probabilistic constraints that produce both sound and tractable problem formulations. We illustrate the effectiveness of our approach on a few examples, including the design of embedded controllers for aircraft power distribution networks.
Range Assignment of Base-Stations Maximizing Coverage Area without Interference
Venue: arXiv cs.CG 2017
Year: 2017

We study the problem of assigning non-overlapping geometric objects centered at a given set of points such that the sum of area covered by them is maximized. If the points are placed on a straight-line and the objects are disks, then the problem is solvable in polynomial time. However, we show that the problem is NP-hard even for simplest objects like disks or squares in ${\mathbb{R}}^2$. Eppstein [CCCG, pages 260--265, 2016] proposed a polynomial time algorithm for maximizing the sum of radii (or perimeter) of non-overlapping balls or disks when the points are arbitrarily placed on a plane. We show that Eppstein's algorithm for maximizing sum of perimeter of the disks in ${\mathbb{R}}^2$ gives a $2$-approximation solution for the sum of area maximization problem. We propose a PTAS for our problem. These approximation results are extendible to higher dimensions. All these approximation results hold for the area maximization problem by regular convex polygons with even number of edges centered at the given points.
Multimodal Machine Learning: A Survey and Taxonomy
Venue: arXiv cs.LG 2017
Year: 2017

Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.
Identifying Critical Risks of Cascading Failures in Power Systems
Venue: arXiv cs.SY 2017
Year: 2017

Potential critical risks of cascading failures in power systems can be identified by exposing those critical electrical elements on which certain initial disturbances may cause maximum disruption to power transmission networks. In this work, we investigate cascading failures in power systems described by the direct current (DC) power flow equations, while initial disturbances take the form of altering admittance of elements. The disruption is quantified with the remaining transmission power at the end of cascading process. In particular, identifying the critical elements and the corresponding initial disturbances causing the worst-case cascading blackout is formulated as a dynamic optimization problem (DOP) in the framework of optimal control theory, where the entire propagation process of cascading failures is put under consideration. An Identifying Critical Risk Algorithm (ICRA) based on the maximum principle is proposed to solve the DOP. Simulation results on the IEEE 9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the effectiveness of the algorithm.
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
Venue: arXiv cs.CV 2017
Year: 2017

We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.
Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting
Venue: arXiv cs.CV 2017
Year: 2017

Identifying the underlying models in a set of data points contaminated by noise and outliers, leads to a highly complex multi-model fitting problem. This problem can be posed as a clustering problem by the projection of higher order affinities between data points into a graph, which can then be clustered using spectral clustering. Calculating all possible higher order affinities is computationally expensive. Hence in most cases only a subset is used. In this paper, we propose an effective sampling method to obtain a highly accurate approximation of the full graph required to solve multi-structural model fitting problems in computer vision. The proposed method is based on the observation that the usefulness of a graph for segmentation improves as the distribution of hypotheses (used to build the graph) approaches the distribution of actual parameters for the given data. In this paper, we approximate this actual parameter distribution using a k-th order statistics based cost function and the samples are generated using a greedy algorithm coupled with a data sub-sampling strategy. The experimental analysis shows that the proposed method is both accurate and computationally efficient compared to the state-of-the-art robust multi-model fitting techniques. The code is publicly available from https://github.com/RuwanT/model-fitting-cbs.